Overview

Dataset statistics

Number of variables20
Number of observations46513
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.1 MiB
Average record size in memory160.0 B

Variable types

Categorical12
Numeric8

Alerts

Project Name has a high cardinality: 2498 distinct valuesHigh cardinality
Address has a high cardinality: 46468 distinct valuesHigh cardinality
Nett Price($) has a high cardinality: 144 distinct valuesHigh cardinality
Sale Date has a high cardinality: 698 distinct valuesHigh cardinality
Tenure has a high cardinality: 576 distinct valuesHigh cardinality
Completion Date has a high cardinality: 75 distinct valuesHigh cardinality
Area (sqm) is highly overall correlated with Transacted Price ($)High correlation
Transacted Price ($) is highly overall correlated with Area (sqm)High correlation
Unit Price ($ psm) is highly overall correlated with Unit Price ($ psf) and 3 other fieldsHigh correlation
Unit Price ($ psf) is highly overall correlated with Unit Price ($ psm) and 3 other fieldsHigh correlation
Postal District is highly overall correlated with Unit Price ($ psm) and 5 other fieldsHigh correlation
Postal Sector is highly overall correlated with Unit Price ($ psm) and 5 other fieldsHigh correlation
Postal Code is highly overall correlated with Unit Price ($ psm) and 5 other fieldsHigh correlation
Type of Area is highly overall correlated with Property Type and 1 other fieldsHigh correlation
Property Type is highly overall correlated with Type of AreaHigh correlation
Completion Date is highly overall correlated with Type of Area and 1 other fieldsHigh correlation
Type of Sale is highly overall correlated with Completion DateHigh correlation
Planning Region is highly overall correlated with Postal District and 3 other fieldsHigh correlation
Planning Area is highly overall correlated with Postal District and 3 other fieldsHigh correlation
Type of Area is highly imbalanced (60.1%)Imbalance
Nett Price($) is highly imbalanced (99.2%)Imbalance
No. of Units is highly skewed (γ1 = 63.42668718)Skewed
Area (sqm) is highly skewed (γ1 = 60.40826919)Skewed
Transacted Price ($) is highly skewed (γ1 = 48.22169828)Skewed
Address is uniformly distributedUniform

Reproduction

Analysis started2023-04-19 12:19:16.960337
Analysis finished2023-04-19 12:19:22.082281
Duration5.12 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Project Name
Categorical

Distinct2498
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size363.5 KiB
N.A.
 
1564
RIVERFRONT RESIDENCES
 
938
RIVERCOVE RESIDENCES
 
620
THE TAPESTRY
 
613
STIRLING RESIDENCES
 
598
Other values (2493)
42180 

Length

Max length47
Median length28
Mean length13.893557
Min length2

Characters and Unicode

Total characters646231
Distinct characters44
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique463 ?
Unique (%)1.0%

Sample

1st rowTHE BAYSHORE
2nd rowKINGSFORD WATERBAY
3rd rowTHE JOVELL
4th rowV ON SHENTON
5th rowTHE BEACON

Common Values

ValueCountFrequency (%)
N.A. 1564
 
3.4%
RIVERFRONT RESIDENCES 938
 
2.0%
RIVERCOVE RESIDENCES 620
 
1.3%
THE TAPESTRY 613
 
1.3%
STIRLING RESIDENCES 598
 
1.3%
PARK COLONIAL 579
 
1.2%
PARC BOTANNIA 535
 
1.2%
HUNDRED PALMS RESIDENCES 531
 
1.1%
KINGSFORD WATERBAY 516
 
1.1%
PARC ESTA 483
 
1.0%
Other values (2488) 39536
85.0%

Length

2023-04-19T20:19:22.110700image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 8018
 
8.1%
residences 7926
 
8.0%
park 3478
 
3.5%
parc 2265
 
2.3%
at 1996
 
2.0%
n.a 1564
 
1.6%
condominium 1068
 
1.1%
1066
 
1.1%
residence 1004
 
1.0%
riverfront 938
 
1.0%
Other values (1884) 69245
70.3%

Most occurring characters

ValueCountFrequency (%)
E 87371
13.5%
R 52577
 
8.1%
A 52342
 
8.1%
52055
 
8.1%
S 48972
 
7.6%
N 45495
 
7.0%
I 45482
 
7.0%
T 38444
 
5.9%
O 28607
 
4.4%
L 23883
 
3.7%
Other values (34) 171003
26.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 586138
90.7%
Space Separator 52055
 
8.1%
Other Punctuation 5034
 
0.8%
Decimal Number 2881
 
0.4%
Dash Punctuation 123
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 87371
14.9%
R 52577
 
9.0%
A 52342
 
8.9%
S 48972
 
8.4%
N 45495
 
7.8%
I 45482
 
7.8%
T 38444
 
6.6%
O 28607
 
4.9%
L 23883
 
4.1%
C 23429
 
4.0%
Other values (16) 139536
23.8%
Decimal Number
ValueCountFrequency (%)
8 780
27.1%
1 423
14.7%
3 405
14.1%
2 353
12.3%
6 242
 
8.4%
5 236
 
8.2%
0 181
 
6.3%
4 146
 
5.1%
7 62
 
2.2%
9 53
 
1.8%
Other Punctuation
ValueCountFrequency (%)
. 3152
62.6%
@ 1332
26.5%
' 509
 
10.1%
& 28
 
0.6%
# 8
 
0.2%
/ 5
 
0.1%
Space Separator
ValueCountFrequency (%)
52055
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 123
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 586138
90.7%
Common 60093
 
9.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 87371
14.9%
R 52577
 
9.0%
A 52342
 
8.9%
S 48972
 
8.4%
N 45495
 
7.8%
I 45482
 
7.8%
T 38444
 
6.6%
O 28607
 
4.9%
L 23883
 
4.1%
C 23429
 
4.0%
Other values (16) 139536
23.8%
Common
ValueCountFrequency (%)
52055
86.6%
. 3152
 
5.2%
@ 1332
 
2.2%
8 780
 
1.3%
' 509
 
0.8%
1 423
 
0.7%
3 405
 
0.7%
2 353
 
0.6%
6 242
 
0.4%
5 236
 
0.4%
Other values (8) 606
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 646231
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 87371
13.5%
R 52577
 
8.1%
A 52342
 
8.1%
52055
 
8.1%
S 48972
 
7.6%
N 45495
 
7.0%
I 45482
 
7.0%
T 38444
 
5.9%
O 28607
 
4.4%
L 23883
 
3.7%
Other values (34) 171003
26.5%

Address
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct46468
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size363.5 KiB
36 Dakota Crescent #14-05
 
2
139 Cavenagh Road #06-06
 
2
21 Meyappa Chettiar Road #03-02
 
2
21 Meyappa Chettiar Road #02-01
 
2
7 Still Lane
 
2
Other values (46463)
46503 

Length

Max length59
Median length47
Mean length26.408423
Min length11

Characters and Unicode

Total characters1228335
Distinct characters69
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46423 ?
Unique (%)99.8%

Sample

1st row22 Bayshore Road #03-02
2nd row66 Upper Serangoon View #16-12
3rd row13 Flora Drive #02-11
4th row5A Shenton Way #44-12
5th row130 Cantonment Road #10-04

Common Values

ValueCountFrequency (%)
36 Dakota Crescent #14-05 2
 
< 0.1%
139 Cavenagh Road #06-06 2
 
< 0.1%
21 Meyappa Chettiar Road #03-02 2
 
< 0.1%
21 Meyappa Chettiar Road #02-01 2
 
< 0.1%
7 Still Lane 2
 
< 0.1%
60 Jalan Lengkok Sembawang 2
 
< 0.1%
12 West Coast Crescent #01-08 2
 
< 0.1%
10 Martin Place #11-13 2
 
< 0.1%
336 River Valley Road #07-01 2
 
< 0.1%
103 Pasir Ris Grove #01-08 2
 
< 0.1%
Other values (46458) 46493
> 99.9%

Length

2023-04-19T20:19:22.170299image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
road 13707
 
6.5%
avenue 6255
 
3.0%
street 4372
 
2.1%
1 3267
 
1.6%
drive 2978
 
1.4%
jalan 1915
 
0.9%
3 1877
 
0.9%
7 1871
 
0.9%
view 1864
 
0.9%
lane 1854
 
0.9%
Other values (5389) 169952
81.0%

Most occurring characters

ValueCountFrequency (%)
251797
20.5%
e 70727
 
5.8%
a 65379
 
5.3%
0 57247
 
4.7%
1 55488
 
4.5%
o 49612
 
4.0%
n 47068
 
3.8%
- 41917
 
3.4%
# 41870
 
3.4%
r 36291
 
3.0%
Other values (59) 510939
41.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 502887
40.9%
Decimal Number 273177
22.2%
Space Separator 251797
20.5%
Uppercase Letter 116064
 
9.4%
Other Punctuation 42425
 
3.5%
Dash Punctuation 41917
 
3.4%
Open Punctuation 34
 
< 0.1%
Close Punctuation 34
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 70727
14.1%
a 65379
13.0%
o 49612
9.9%
n 47068
9.4%
r 36291
 
7.2%
i 31794
 
6.3%
t 26913
 
5.4%
l 22095
 
4.4%
d 21179
 
4.2%
s 20865
 
4.1%
Other values (16) 110964
22.1%
Uppercase Letter
ValueCountFrequency (%)
R 17187
14.8%
S 13035
11.2%
C 11067
 
9.5%
A 9599
 
8.3%
L 7167
 
6.2%
P 6397
 
5.5%
B 5818
 
5.0%
T 5628
 
4.8%
W 4946
 
4.3%
D 4337
 
3.7%
Other values (15) 30883
26.6%
Decimal Number
ValueCountFrequency (%)
0 57247
21.0%
1 55488
20.3%
2 33944
12.4%
3 27423
10.0%
5 20054
 
7.3%
4 18096
 
6.6%
6 18084
 
6.6%
8 15554
 
5.7%
7 14944
 
5.5%
9 12343
 
4.5%
Other Punctuation
ValueCountFrequency (%)
# 41870
98.7%
. 265
 
0.6%
' 182
 
0.4%
/ 108
 
0.3%
Space Separator
ValueCountFrequency (%)
251797
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 41917
100.0%
Open Punctuation
ValueCountFrequency (%)
( 34
100.0%
Close Punctuation
ValueCountFrequency (%)
) 34
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 618951
50.4%
Common 609384
49.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 70727
 
11.4%
a 65379
 
10.6%
o 49612
 
8.0%
n 47068
 
7.6%
r 36291
 
5.9%
i 31794
 
5.1%
t 26913
 
4.3%
l 22095
 
3.6%
d 21179
 
3.4%
s 20865
 
3.4%
Other values (41) 227028
36.7%
Common
ValueCountFrequency (%)
251797
41.3%
0 57247
 
9.4%
1 55488
 
9.1%
- 41917
 
6.9%
# 41870
 
6.9%
2 33944
 
5.6%
3 27423
 
4.5%
5 20054
 
3.3%
4 18096
 
3.0%
6 18084
 
3.0%
Other values (8) 43464
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1228335
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
251797
20.5%
e 70727
 
5.8%
a 65379
 
5.3%
0 57247
 
4.7%
1 55488
 
4.5%
o 49612
 
4.0%
n 47068
 
3.8%
- 41917
 
3.4%
# 41870
 
3.4%
r 36291
 
3.0%
Other values (59) 510939
41.6%

No. of Units
Real number (ℝ)

Distinct53
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1268248
Minimum1
Maximum560
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size363.5 KiB
2023-04-19T20:19:22.228748image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum560
Range559
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.3747962
Coefficient of variation (CV)4.7698599
Kurtosis4844.7169
Mean1.1268248
Median Absolute Deviation (MAD)0
Skewness63.426687
Sum52412
Variance28.888434
MonotonicityNot monotonic
2023-04-19T20:19:22.427365image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 46427
99.8%
2 12
 
< 0.1%
20 5
 
< 0.1%
32 4
 
< 0.1%
3 4
 
< 0.1%
4 3
 
< 0.1%
11 3
 
< 0.1%
12 3
 
< 0.1%
14 2
 
< 0.1%
6 2
 
< 0.1%
Other values (43) 48
 
0.1%
ValueCountFrequency (%)
1 46427
99.8%
2 12
 
< 0.1%
3 4
 
< 0.1%
4 3
 
< 0.1%
5 1
 
< 0.1%
6 2
 
< 0.1%
8 2
 
< 0.1%
11 3
 
< 0.1%
12 3
 
< 0.1%
13 1
 
< 0.1%
ValueCountFrequency (%)
560 1
< 0.1%
436 1
< 0.1%
336 1
< 0.1%
330 1
< 0.1%
290 1
< 0.1%
288 1
< 0.1%
286 1
< 0.1%
244 1
< 0.1%
210 1
< 0.1%
200 1
< 0.1%

Area (sqm)
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct862
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean140.18962
Minimum24
Maximum87986
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size363.5 KiB
2023-04-19T20:19:22.482229image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile44
Q170
median98
Q3129
95-th percentile290
Maximum87986
Range87962
Interquartile range (IQR)59

Descriptive statistics

Standard deviation841.24481
Coefficient of variation (CV)6.0007637
Kurtosis4503.7
Mean140.18962
Median Absolute Deviation (MAD)29
Skewness60.408269
Sum6520640
Variance707692.83
MonotonicityNot monotonic
2023-04-19T20:19:22.532414image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43 709
 
1.5%
103 680
 
1.5%
89 670
 
1.4%
91 625
 
1.3%
85 596
 
1.3%
84 590
 
1.3%
99 581
 
1.2%
98 556
 
1.2%
65 548
 
1.2%
57 543
 
1.2%
Other values (852) 40415
86.9%
ValueCountFrequency (%)
24 1
 
< 0.1%
30 3
 
< 0.1%
31 12
 
< 0.1%
32 23
 
< 0.1%
33 33
 
0.1%
34 68
0.1%
35 39
0.1%
36 54
0.1%
37 96
0.2%
38 78
0.2%
ValueCountFrequency (%)
87986 1
< 0.1%
58460 1
< 0.1%
52381 1
< 0.1%
51892 1
< 0.1%
48347 1
< 0.1%
44541 1
< 0.1%
38717 1
< 0.1%
37636 1
< 0.1%
35574 1
< 0.1%
33920 1
< 0.1%

Type of Area
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size363.5 KiB
Strata
42834 
Land
 
3679

Length

Max length6
Median length6
Mean length5.8418077
Min length4

Characters and Unicode

Total characters271720
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStrata
2nd rowStrata
3rd rowStrata
4th rowStrata
5th rowStrata

Common Values

ValueCountFrequency (%)
Strata 42834
92.1%
Land 3679
 
7.9%

Length

2023-04-19T20:19:22.580964image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-19T20:19:22.625268image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
strata 42834
92.1%
land 3679
 
7.9%

Most occurring characters

ValueCountFrequency (%)
a 89347
32.9%
t 85668
31.5%
S 42834
15.8%
r 42834
15.8%
L 3679
 
1.4%
n 3679
 
1.4%
d 3679
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 225207
82.9%
Uppercase Letter 46513
 
17.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 89347
39.7%
t 85668
38.0%
r 42834
19.0%
n 3679
 
1.6%
d 3679
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
S 42834
92.1%
L 3679
 
7.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 271720
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 89347
32.9%
t 85668
31.5%
S 42834
15.8%
r 42834
15.8%
L 3679
 
1.4%
n 3679
 
1.4%
d 3679
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 271720
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 89347
32.9%
t 85668
31.5%
S 42834
15.8%
r 42834
15.8%
L 3679
 
1.4%
n 3679
 
1.4%
d 3679
 
1.4%

Transacted Price ($)
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct9824
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2087292.7
Minimum358000
Maximum9.8 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size363.5 KiB
2023-04-19T20:19:22.666974image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum358000
5-th percentile682000
Q1930000
median1267000
Q31840292
95-th percentile4036560
Maximum9.8 × 108
Range9.79642 × 108
Interquartile range (IQR)910292

Descriptive statistics

Standard deviation14186311
Coefficient of variation (CV)6.7965123
Kurtosis2677.4329
Mean2087292.7
Median Absolute Deviation (MAD)397000
Skewness48.221698
Sum9.7086247 × 1010
Variance2.0125141 × 1014
MonotonicityNot monotonic
2023-04-19T20:19:22.722730image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1200000 313
 
0.7%
1100000 304
 
0.7%
1300000 272
 
0.6%
1050000 261
 
0.6%
1500000 258
 
0.6%
1150000 255
 
0.5%
1400000 227
 
0.5%
1350000 215
 
0.5%
1250000 210
 
0.5%
1180000 207
 
0.4%
Other values (9814) 43991
94.6%
ValueCountFrequency (%)
358000 1
< 0.1%
362000 1
< 0.1%
369000 1
< 0.1%
371000 1
< 0.1%
373000 1
< 0.1%
376000 2
< 0.1%
380000 1
< 0.1%
383000 1
< 0.1%
388000 1
< 0.1%
392000 2
< 0.1%
ValueCountFrequency (%)
980000000 1
< 0.1%
970000000 1
< 0.1%
906889000 1
< 0.1%
906700000 1
< 0.1%
840888888 1
< 0.1%
765781819 1
< 0.1%
728000000 1
< 0.1%
629000000 1
< 0.1%
610000000 1
< 0.1%
575000000 1
< 0.1%

Nett Price($)
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct144
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size363.5 KiB
-
46363 
895000
 
2
889000
 
2
887000
 
2
802700
 
2
Other values (139)
 
142

Length

Max length7
Median length1
Mean length1.01778
Min length1

Characters and Unicode

Total characters47340
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique136 ?
Unique (%)0.3%

Sample

1st row-
2nd row-
3rd row-
4th row2847680
5th row-

Common Values

ValueCountFrequency (%)
- 46363
99.7%
895000 2
 
< 0.1%
889000 2
 
< 0.1%
887000 2
 
< 0.1%
802700 2
 
< 0.1%
1296000 2
 
< 0.1%
853000 2
 
< 0.1%
868420 2
 
< 0.1%
842000 1
 
< 0.1%
936000 1
 
< 0.1%
Other values (134) 134
 
0.3%

Length

2023-04-19T20:19:22.772626image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
46363
99.7%
889000 2
 
< 0.1%
887000 2
 
< 0.1%
802700 2
 
< 0.1%
1296000 2
 
< 0.1%
853000 2
 
< 0.1%
868420 2
 
< 0.1%
895000 2
 
< 0.1%
2644505 1
 
< 0.1%
850000 1
 
< 0.1%
Other values (134) 134
 
0.3%

Most occurring characters

ValueCountFrequency (%)
- 46363
97.9%
0 335
 
0.7%
1 121
 
0.3%
8 92
 
0.2%
4 72
 
0.2%
9 69
 
0.1%
7 66
 
0.1%
2 63
 
0.1%
3 58
 
0.1%
5 54
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Dash Punctuation 46363
97.9%
Decimal Number 977
 
2.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 335
34.3%
1 121
 
12.4%
8 92
 
9.4%
4 72
 
7.4%
9 69
 
7.1%
7 66
 
6.8%
2 63
 
6.4%
3 58
 
5.9%
5 54
 
5.5%
6 47
 
4.8%
Dash Punctuation
ValueCountFrequency (%)
- 46363
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 47340
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 46363
97.9%
0 335
 
0.7%
1 121
 
0.3%
8 92
 
0.2%
4 72
 
0.2%
9 69
 
0.1%
7 66
 
0.1%
2 63
 
0.1%
3 58
 
0.1%
5 54
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47340
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 46363
97.9%
0 335
 
0.7%
1 121
 
0.3%
8 92
 
0.2%
4 72
 
0.2%
9 69
 
0.1%
7 66
 
0.1%
2 63
 
0.1%
3 58
 
0.1%
5 54
 
0.1%

Unit Price ($ psm)
Real number (ℝ)

Distinct15812
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14759.852
Minimum1503
Maximum54363
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size363.5 KiB
2023-04-19T20:19:22.821374image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1503
5-th percentile8010
Q110714
median14190
Q317647
95-th percentile24501.6
Maximum54363
Range52860
Interquartile range (IQR)6933

Descriptive statistics

Standard deviation5424.0907
Coefficient of variation (CV)0.36748951
Kurtosis2.8702761
Mean14759.852
Median Absolute Deviation (MAD)3469
Skewness1.2611516
Sum6.8652498 × 108
Variance29420759
MonotonicityNot monotonic
2023-04-19T20:19:22.875724image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 137
 
0.3%
15000 86
 
0.2%
16667 78
 
0.2%
12500 75
 
0.2%
20000 69
 
0.1%
13333 61
 
0.1%
14286 57
 
0.1%
14000 56
 
0.1%
16000 50
 
0.1%
11111 49
 
0.1%
Other values (15802) 45795
98.5%
ValueCountFrequency (%)
1503 1
< 0.1%
1802 1
< 0.1%
1809 1
< 0.1%
2131 1
< 0.1%
2433 1
< 0.1%
2659 1
< 0.1%
2771 1
< 0.1%
3032 1
< 0.1%
3056 1
< 0.1%
3251 1
< 0.1%
ValueCountFrequency (%)
54363 1
< 0.1%
53030 1
< 0.1%
49835 1
< 0.1%
49080 1
< 0.1%
46667 1
< 0.1%
46281 1
< 0.1%
44984 1
< 0.1%
44940 1
< 0.1%
44892 1
< 0.1%
44433 1
< 0.1%

Unit Price ($ psf)
Real number (ℝ)

Distinct2840
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1371.223
Minimum140
Maximum5050
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size363.5 KiB
2023-04-19T20:19:22.928830image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum140
5-th percentile744
Q1995
median1318
Q31639
95-th percentile2276.4
Maximum5050
Range4910
Interquartile range (IQR)644

Descriptive statistics

Standard deviation503.9075
Coefficient of variation (CV)0.36748764
Kurtosis2.8704244
Mean1371.223
Median Absolute Deviation (MAD)322
Skewness1.2611819
Sum63779695
Variance253922.77
MonotonicityNot monotonic
2023-04-19T20:19:22.987609image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
929 139
 
0.3%
1548 103
 
0.2%
1394 93
 
0.2%
1327 84
 
0.2%
1161 79
 
0.2%
1858 73
 
0.2%
1301 72
 
0.2%
1032 72
 
0.2%
1239 72
 
0.2%
1340 68
 
0.1%
Other values (2830) 45658
98.2%
ValueCountFrequency (%)
140 1
< 0.1%
167 1
< 0.1%
168 1
< 0.1%
198 1
< 0.1%
226 1
< 0.1%
247 1
< 0.1%
257 1
< 0.1%
282 1
< 0.1%
284 1
< 0.1%
302 1
< 0.1%
ValueCountFrequency (%)
5050 1
< 0.1%
4927 1
< 0.1%
4630 1
< 0.1%
4560 1
< 0.1%
4335 1
< 0.1%
4300 1
< 0.1%
4179 1
< 0.1%
4175 1
< 0.1%
4171 1
< 0.1%
4128 1
< 0.1%

Sale Date
Categorical

Distinct698
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size363.5 KiB
05-JUL-2018
 
1000
22-JUL-2017
 
591
14-APR-2018
 
466
05-MAY-2018
 
377
05-AUG-2017
 
309
Other values (693)
43770 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters511643
Distinct characters30
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row28-FEB-2019
2nd row28-FEB-2019
3rd row28-FEB-2019
4th row28-FEB-2019
5th row28-FEB-2019

Common Values

ValueCountFrequency (%)
05-JUL-2018 1000
 
2.1%
22-JUL-2017 591
 
1.3%
14-APR-2018 466
 
1.0%
05-MAY-2018 377
 
0.8%
05-AUG-2017 309
 
0.7%
22-SEP-2018 309
 
0.7%
24-MAR-2018 307
 
0.7%
23-MAR-2019 249
 
0.5%
11-NOV-2017 236
 
0.5%
07-APR-2018 229
 
0.5%
Other values (688) 42440
91.2%

Length

2023-04-19T20:19:23.035858image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
05-jul-2018 1000
 
2.1%
22-jul-2017 591
 
1.3%
14-apr-2018 466
 
1.0%
05-may-2018 377
 
0.8%
05-aug-2017 309
 
0.7%
22-sep-2018 309
 
0.7%
24-mar-2018 307
 
0.7%
23-mar-2019 249
 
0.5%
11-nov-2017 236
 
0.5%
07-apr-2018 229
 
0.5%
Other values (688) 42440
91.2%

Most occurring characters

ValueCountFrequency (%)
- 93026
18.2%
2 67174
13.1%
1 66203
12.9%
0 65105
12.7%
8 27940
 
5.5%
7 23499
 
4.6%
A 19398
 
3.8%
U 14258
 
2.8%
J 12877
 
2.5%
N 11006
 
2.2%
Other values (20) 111157
21.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 279078
54.5%
Uppercase Letter 139539
27.3%
Dash Punctuation 93026
 
18.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 19398
13.9%
U 14258
10.2%
J 12877
 
9.2%
N 11006
 
7.9%
M 9423
 
6.8%
E 9333
 
6.7%
O 7690
 
5.5%
R 6971
 
5.0%
P 6673
 
4.8%
C 6540
 
4.7%
Other values (9) 35370
25.3%
Decimal Number
ValueCountFrequency (%)
2 67174
24.1%
1 66203
23.7%
0 65105
23.3%
8 27940
10.0%
7 23499
 
8.4%
9 7950
 
2.8%
3 6751
 
2.4%
5 5574
 
2.0%
4 4726
 
1.7%
6 4156
 
1.5%
Dash Punctuation
ValueCountFrequency (%)
- 93026
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 372104
72.7%
Latin 139539
 
27.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 19398
13.9%
U 14258
10.2%
J 12877
 
9.2%
N 11006
 
7.9%
M 9423
 
6.8%
E 9333
 
6.7%
O 7690
 
5.5%
R 6971
 
5.0%
P 6673
 
4.8%
C 6540
 
4.7%
Other values (9) 35370
25.3%
Common
ValueCountFrequency (%)
- 93026
25.0%
2 67174
18.1%
1 66203
17.8%
0 65105
17.5%
8 27940
 
7.5%
7 23499
 
6.3%
9 7950
 
2.1%
3 6751
 
1.8%
5 5574
 
1.5%
4 4726
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 511643
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 93026
18.2%
2 67174
13.1%
1 66203
12.9%
0 65105
12.7%
8 27940
 
5.5%
7 23499
 
4.6%
A 19398
 
3.8%
U 14258
 
2.8%
J 12877
 
2.5%
N 11006
 
2.2%
Other values (20) 111157
21.7%

Property Type
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size363.5 KiB
Condominium
22283 
Apartment
15225 
Executive Condominium
4562 
Terrace House
2760 
Semi-Detached House
 
1225

Length

Max length21
Median length19
Mean length11.685056
Min length9

Characters and Unicode

Total characters543507
Distinct characters25
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCondominium
2nd rowApartment
3rd rowCondominium
4th rowApartment
5th rowApartment

Common Values

ValueCountFrequency (%)
Condominium 22283
47.9%
Apartment 15225
32.7%
Executive Condominium 4562
 
9.8%
Terrace House 2760
 
5.9%
Semi-Detached House 1225
 
2.6%
Detached House 458
 
1.0%

Length

2023-04-19T20:19:23.080038image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-19T20:19:23.128811image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
condominium 26845
48.4%
apartment 15225
27.4%
executive 4562
 
8.2%
house 4443
 
8.0%
terrace 2760
 
5.0%
semi-detached 1225
 
2.2%
detached 458
 
0.8%

Most occurring characters

ValueCountFrequency (%)
m 70140
12.9%
n 68915
12.7%
i 59477
10.9%
o 58133
10.7%
e 38903
 
7.2%
t 36695
 
6.8%
u 35850
 
6.6%
d 28528
 
5.2%
C 26845
 
4.9%
r 20745
 
3.8%
Other values (15) 99276
18.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 476534
87.7%
Uppercase Letter 56743
 
10.4%
Space Separator 9005
 
1.7%
Dash Punctuation 1225
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m 70140
14.7%
n 68915
14.5%
i 59477
12.5%
o 58133
12.2%
e 38903
8.2%
t 36695
7.7%
u 35850
7.5%
d 28528
6.0%
r 20745
 
4.4%
a 19668
 
4.1%
Other values (6) 39480
8.3%
Uppercase Letter
ValueCountFrequency (%)
C 26845
47.3%
A 15225
26.8%
E 4562
 
8.0%
H 4443
 
7.8%
T 2760
 
4.9%
D 1683
 
3.0%
S 1225
 
2.2%
Space Separator
ValueCountFrequency (%)
9005
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1225
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 533277
98.1%
Common 10230
 
1.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
m 70140
13.2%
n 68915
12.9%
i 59477
11.2%
o 58133
10.9%
e 38903
7.3%
t 36695
6.9%
u 35850
6.7%
d 28528
 
5.3%
C 26845
 
5.0%
r 20745
 
3.9%
Other values (13) 89046
16.7%
Common
ValueCountFrequency (%)
9005
88.0%
- 1225
 
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 543507
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
m 70140
12.9%
n 68915
12.7%
i 59477
10.9%
o 58133
10.7%
e 38903
 
7.2%
t 36695
 
6.8%
u 35850
 
6.6%
d 28528
 
5.2%
C 26845
 
4.9%
r 20745
 
3.8%
Other values (15) 99276
18.3%

Tenure
Categorical

Distinct576
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size363.5 KiB
Freehold
14231 
99 Yrs From 31/05/2018
 
938
99 Yrs From 05/12/2016
 
620
99 Yrs From 31/07/2017
 
613
99 Yrs From 25/08/2014
 
599
Other values (571)
29512 

Length

Max length26
Median length22
Mean length17.733687
Min length4

Characters and Unicode

Total characters824847
Distinct characters27
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique71 ?
Unique (%)0.2%

Sample

1st row99 Yrs From 31/05/1993
2nd row99 Yrs From 03/03/2014
3rd row99 Yrs From 08/03/2018
4th row99 Yrs From 29/11/2011
5th row99 Yrs From 05/03/2004

Common Values

ValueCountFrequency (%)
Freehold 14231
30.6%
99 Yrs From 31/05/2018 938
 
2.0%
99 Yrs From 05/12/2016 620
 
1.3%
99 Yrs From 31/07/2017 613
 
1.3%
99 Yrs From 25/08/2014 599
 
1.3%
99 Yrs From 18/08/2017 598
 
1.3%
99 Yrs From 11/10/2017 579
 
1.2%
99 Yrs From 28/12/2016 535
 
1.2%
99 Yrs From 30/05/2016 531
 
1.1%
99 Yrs From 03/03/2014 516
 
1.1%
Other values (566) 26753
57.5%

Length

2023-04-19T20:19:23.178049image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
from 31984
22.4%
yrs 31984
22.4%
99 30299
21.2%
freehold 14231
9.9%
999 1599
 
1.1%
31/05/2018 938
 
0.7%
05/12/2016 620
 
0.4%
31/07/2017 613
 
0.4%
25/08/2014 599
 
0.4%
18/08/2017 598
 
0.4%
Other values (579) 29590
20.7%

Most occurring characters

ValueCountFrequency (%)
96576
11.7%
9 80949
 
9.8%
r 78494
 
9.5%
0 70683
 
8.6%
/ 63968
 
7.8%
1 58583
 
7.1%
o 46510
 
5.6%
F 46215
 
5.6%
2 45046
 
5.5%
s 32574
 
3.9%
Other values (17) 205249
24.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 322425
39.1%
Lowercase Letter 263077
31.9%
Space Separator 96576
 
11.7%
Uppercase Letter 78795
 
9.6%
Other Punctuation 63974
 
7.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 80949
25.1%
0 70683
21.9%
1 58583
18.2%
2 45046
14.0%
8 14533
 
4.5%
5 12936
 
4.0%
7 11704
 
3.6%
3 10897
 
3.4%
6 9850
 
3.1%
4 7244
 
2.2%
Lowercase Letter
ValueCountFrequency (%)
r 78494
29.8%
o 46510
17.7%
s 32574
12.4%
m 31984
12.2%
e 29347
 
11.2%
d 14526
 
5.5%
h 14526
 
5.5%
l 14526
 
5.5%
a 590
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
F 46215
58.7%
Y 32279
41.0%
L 295
 
0.4%
N 3
 
< 0.1%
A 3
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
/ 63968
> 99.9%
. 6
 
< 0.1%
Space Separator
ValueCountFrequency (%)
96576
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 482975
58.6%
Latin 341872
41.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 78494
23.0%
o 46510
13.6%
F 46215
13.5%
s 32574
9.5%
Y 32279
9.4%
m 31984
9.4%
e 29347
 
8.6%
d 14526
 
4.2%
h 14526
 
4.2%
l 14526
 
4.2%
Other values (4) 891
 
0.3%
Common
ValueCountFrequency (%)
96576
20.0%
9 80949
16.8%
0 70683
14.6%
/ 63968
13.2%
1 58583
12.1%
2 45046
9.3%
8 14533
 
3.0%
5 12936
 
2.7%
7 11704
 
2.4%
3 10897
 
2.3%
Other values (3) 17100
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 824847
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
96576
11.7%
9 80949
 
9.8%
r 78494
 
9.5%
0 70683
 
8.6%
/ 63968
 
7.8%
1 58583
 
7.1%
o 46510
 
5.6%
F 46215
 
5.6%
2 45046
 
5.5%
s 32574
 
3.9%
Other values (17) 205249
24.9%

Completion Date
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct75
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size363.5 KiB
Uncompleted
17282 
2017
2625 
2014
 
2014
2015
 
1987
2016
 
1896
Other values (70)
20709 

Length

Max length11
Median length4
Mean length6.6955475
Min length4

Characters and Unicode

Total characters311430
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row1996
2nd row2018
3rd rowUncompleted
4th row2017
5th row2008

Common Values

ValueCountFrequency (%)
Uncompleted 17282
37.2%
2017 2625
 
5.6%
2014 2014
 
4.3%
2015 1987
 
4.3%
2016 1896
 
4.1%
Unknown 1468
 
3.2%
2013 1454
 
3.1%
2011 1235
 
2.7%
2009 1027
 
2.2%
2000 1018
 
2.2%
Other values (65) 14507
31.2%

Length

2023-04-19T20:19:23.223506image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
uncompleted 17282
37.2%
2017 2625
 
5.6%
2014 2014
 
4.3%
2015 1987
 
4.3%
2016 1896
 
4.1%
unknown 1468
 
3.2%
2013 1454
 
3.1%
2011 1235
 
2.7%
2009 1027
 
2.2%
2000 1018
 
2.2%
Other values (65) 14507
31.2%

Most occurring characters

ValueCountFrequency (%)
e 34564
 
11.1%
0 30873
 
9.9%
2 23009
 
7.4%
1 22302
 
7.2%
n 21686
 
7.0%
U 18750
 
6.0%
o 18750
 
6.0%
l 17282
 
5.5%
p 17282
 
5.5%
t 17282
 
5.5%
Other values (12) 89650
28.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 181628
58.3%
Decimal Number 111052
35.7%
Uppercase Letter 18750
 
6.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 34564
19.0%
n 21686
11.9%
o 18750
10.3%
l 17282
9.5%
p 17282
9.5%
t 17282
9.5%
d 17282
9.5%
m 17282
9.5%
c 17282
9.5%
k 1468
 
0.8%
Decimal Number
ValueCountFrequency (%)
0 30873
27.8%
2 23009
20.7%
1 22302
20.1%
9 13511
12.2%
7 4808
 
4.3%
8 3642
 
3.3%
5 3628
 
3.3%
4 3450
 
3.1%
6 3414
 
3.1%
3 2415
 
2.2%
Uppercase Letter
ValueCountFrequency (%)
U 18750
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 200378
64.3%
Common 111052
35.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 34564
17.2%
n 21686
10.8%
U 18750
9.4%
o 18750
9.4%
l 17282
8.6%
p 17282
8.6%
t 17282
8.6%
d 17282
8.6%
m 17282
8.6%
c 17282
8.6%
Other values (2) 2936
 
1.5%
Common
ValueCountFrequency (%)
0 30873
27.8%
2 23009
20.7%
1 22302
20.1%
9 13511
12.2%
7 4808
 
4.3%
8 3642
 
3.3%
5 3628
 
3.3%
4 3450
 
3.1%
6 3414
 
3.1%
3 2415
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 311430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 34564
 
11.1%
0 30873
 
9.9%
2 23009
 
7.4%
1 22302
 
7.2%
n 21686
 
7.0%
U 18750
 
6.0%
o 18750
 
6.0%
l 17282
 
5.5%
p 17282
 
5.5%
t 17282
 
5.5%
Other values (12) 89650
28.8%

Type of Sale
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size363.5 KiB
Resale
26302 
New Sale
19593 
Sub Sale
 
618

Length

Max length8
Median length6
Mean length6.8690474
Min length6

Characters and Unicode

Total characters319500
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowResale
2nd rowSub Sale
3rd rowNew Sale
4th rowNew Sale
5th rowResale

Common Values

ValueCountFrequency (%)
Resale 26302
56.5%
New Sale 19593
42.1%
Sub Sale 618
 
1.3%

Length

2023-04-19T20:19:23.273133image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-19T20:19:23.322011image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
resale 26302
39.4%
sale 20211
30.3%
new 19593
29.4%
sub 618
 
0.9%

Most occurring characters

ValueCountFrequency (%)
e 92408
28.9%
a 46513
14.6%
l 46513
14.6%
R 26302
 
8.2%
s 26302
 
8.2%
S 20829
 
6.5%
20211
 
6.3%
N 19593
 
6.1%
w 19593
 
6.1%
u 618
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 232565
72.8%
Uppercase Letter 66724
 
20.9%
Space Separator 20211
 
6.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 92408
39.7%
a 46513
20.0%
l 46513
20.0%
s 26302
 
11.3%
w 19593
 
8.4%
u 618
 
0.3%
b 618
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
R 26302
39.4%
S 20829
31.2%
N 19593
29.4%
Space Separator
ValueCountFrequency (%)
20211
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 299289
93.7%
Common 20211
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 92408
30.9%
a 46513
15.5%
l 46513
15.5%
R 26302
 
8.8%
s 26302
 
8.8%
S 20829
 
7.0%
N 19593
 
6.5%
w 19593
 
6.5%
u 618
 
0.2%
b 618
 
0.2%
Common
ValueCountFrequency (%)
20211
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 319500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 92408
28.9%
a 46513
14.6%
l 46513
14.6%
R 26302
 
8.2%
s 26302
 
8.2%
S 20829
 
6.5%
20211
 
6.3%
N 19593
 
6.1%
w 19593
 
6.1%
u 618
 
0.2%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size363.5 KiB
Private
23438 
HDB
16758 
N.A
6317 

Length

Max length7
Median length7
Mean length5.0156085
Min length3

Characters and Unicode

Total characters233291
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrivate
2nd rowPrivate
3rd rowN.A
4th rowN.A
5th rowPrivate

Common Values

ValueCountFrequency (%)
Private 23438
50.4%
HDB 16758
36.0%
N.A 6317
 
13.6%

Length

2023-04-19T20:19:23.364756image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-19T20:19:23.414780image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
private 23438
50.4%
hdb 16758
36.0%
n.a 6317
 
13.6%

Most occurring characters

ValueCountFrequency (%)
P 23438
10.0%
r 23438
10.0%
i 23438
10.0%
v 23438
10.0%
a 23438
10.0%
t 23438
10.0%
e 23438
10.0%
H 16758
7.2%
D 16758
7.2%
B 16758
7.2%
Other values (3) 18951
8.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 140628
60.3%
Uppercase Letter 86346
37.0%
Other Punctuation 6317
 
2.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 23438
27.1%
H 16758
19.4%
D 16758
19.4%
B 16758
19.4%
N 6317
 
7.3%
A 6317
 
7.3%
Lowercase Letter
ValueCountFrequency (%)
r 23438
16.7%
i 23438
16.7%
v 23438
16.7%
a 23438
16.7%
t 23438
16.7%
e 23438
16.7%
Other Punctuation
ValueCountFrequency (%)
. 6317
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 226974
97.3%
Common 6317
 
2.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 23438
10.3%
r 23438
10.3%
i 23438
10.3%
v 23438
10.3%
a 23438
10.3%
t 23438
10.3%
e 23438
10.3%
H 16758
7.4%
D 16758
7.4%
B 16758
7.4%
Other values (2) 12634
5.6%
Common
ValueCountFrequency (%)
. 6317
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 233291
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 23438
10.0%
r 23438
10.0%
i 23438
10.0%
v 23438
10.0%
a 23438
10.0%
t 23438
10.0%
e 23438
10.0%
H 16758
7.2%
D 16758
7.2%
B 16758
7.2%
Other values (3) 18951
8.1%

Postal District
Real number (ℝ)

Distinct27
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.263819
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size363.5 KiB
2023-04-19T20:19:23.454577image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q110
median16
Q319
95-th percentile27
Maximum28
Range27
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.9821349
Coefficient of variation (CV)0.45743041
Kurtosis-0.73023178
Mean15.263819
Median Absolute Deviation (MAD)5
Skewness-0.16397876
Sum709966
Variance48.750208
MonotonicityNot monotonic
2023-04-19T20:19:23.505712image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
19 6902
14.8%
15 3382
 
7.3%
5 2901
 
6.2%
18 2788
 
6.0%
3 2705
 
5.8%
23 2693
 
5.8%
14 2672
 
5.7%
10 2603
 
5.6%
27 2449
 
5.3%
9 2324
 
5.0%
Other values (17) 15094
32.5%
ValueCountFrequency (%)
1 426
 
0.9%
2 482
 
1.0%
3 2705
5.8%
4 547
 
1.2%
5 2901
6.2%
6 2
 
< 0.1%
7 224
 
0.5%
8 523
 
1.1%
9 2324
5.0%
10 2603
5.6%
ValueCountFrequency (%)
28 1305
 
2.8%
27 2449
 
5.3%
26 433
 
0.9%
25 646
 
1.4%
23 2693
 
5.8%
22 819
 
1.8%
21 1412
 
3.0%
20 1409
 
3.0%
19 6902
14.8%
18 2788
6.0%

Postal Sector
Real number (ℝ)

Distinct71
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.48365
Minimum1
Maximum82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size363.5 KiB
2023-04-19T20:19:23.561216image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12
Q126
median46
Q356
95-th percentile76
Maximum82
Range81
Interquartile range (IQR)30

Descriptive statistics

Standard deviation20.443256
Coefficient of variation (CV)0.47013662
Kurtosis-0.91740466
Mean43.48365
Median Absolute Deviation (MAD)14
Skewness-0.052798936
Sum2022555
Variance417.92672
MonotonicityNot monotonic
2023-04-19T20:19:23.618594image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53 2898
 
6.2%
54 2394
 
5.1%
12 2020
 
4.3%
52 1916
 
4.1%
76 1798
 
3.9%
14 1421
 
3.1%
35 1399
 
3.0%
23 1374
 
3.0%
43 1347
 
2.9%
57 1211
 
2.6%
Other values (61) 28735
61.8%
ValueCountFrequency (%)
1 289
 
0.6%
5 13
 
< 0.1%
6 124
 
0.3%
7 322
 
0.7%
8 160
 
0.3%
9 419
 
0.9%
10 128
 
0.3%
11 732
 
1.6%
12 2020
4.3%
13 149
 
0.3%
ValueCountFrequency (%)
82 539
 
1.2%
80 556
 
1.2%
79 749
1.6%
78 405
 
0.9%
77 28
 
0.1%
76 1798
3.9%
75 651
 
1.4%
73 646
 
1.4%
68 979
2.1%
67 535
 
1.2%

Postal Code
Real number (ℝ)

Distinct9256
Distinct (%)19.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean442652.27
Minimum18965
Maximum829750
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size363.5 KiB
2023-04-19T20:19:23.682623image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum18965
5-th percentile126751
Q1266120
median465462
Q3567752
95-th percentile769473
Maximum829750
Range810785
Interquartile range (IQR)301632

Descriptive statistics

Standard deviation204006.37
Coefficient of variation (CV)0.46087274
Kurtosis-0.90987655
Mean442652.27
Median Absolute Deviation (MAD)137481
Skewness-0.047959029
Sum2.0589085 × 1010
Variance4.1618597 × 1010
MonotonicityNot monotonic
2023-04-19T20:19:23.740680image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126756 296
 
0.6%
148960 294
 
0.6%
148962 278
 
0.6%
158745 203
 
0.4%
149456 185
 
0.4%
237963 180
 
0.4%
357688 174
 
0.4%
149457 168
 
0.4%
126754 167
 
0.4%
533813 167
 
0.4%
Other values (9246) 44401
95.5%
ValueCountFrequency (%)
18965 3
 
< 0.1%
18978 107
0.2%
18979 82
0.2%
18980 25
 
0.1%
18985 32
 
0.1%
18987 40
 
0.1%
58416 2
 
< 0.1%
59108 11
 
< 0.1%
68803 37
 
0.1%
68814 42
 
0.1%
ValueCountFrequency (%)
829750 1
 
< 0.1%
829748 1
 
< 0.1%
829698 1
 
< 0.1%
829697 1
 
< 0.1%
829676 1
 
< 0.1%
829653 1
 
< 0.1%
829513 1
 
< 0.1%
829488 1
 
< 0.1%
828843 2
 
< 0.1%
828842 6
< 0.1%

Planning Region
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size363.5 KiB
Central Region
21141 
North East Region
8898 
East Region
7309 
West Region
5900 
North Region
3265 

Length

Max length17
Median length14
Mean length13.581558
Min length11

Characters and Unicode

Total characters631719
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEast Region
2nd rowNorth East Region
3rd rowEast Region
4th rowCentral Region
5th rowCentral Region

Common Values

ValueCountFrequency (%)
Central Region 21141
45.5%
North East Region 8898
19.1%
East Region 7309
 
15.7%
West Region 5900
 
12.7%
North Region 3265
 
7.0%

Length

2023-04-19T20:19:23.796093image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-19T20:19:23.843711image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
region 46513
45.6%
central 21141
20.7%
east 16207
 
15.9%
north 12163
 
11.9%
west 5900
 
5.8%

Most occurring characters

ValueCountFrequency (%)
e 73554
11.6%
n 67654
10.7%
o 58676
9.3%
t 55411
8.8%
55411
8.8%
R 46513
7.4%
g 46513
7.4%
i 46513
7.4%
a 37348
 
5.9%
r 33304
 
5.3%
Other values (7) 110822
17.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 474384
75.1%
Uppercase Letter 101924
 
16.1%
Space Separator 55411
 
8.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 73554
15.5%
n 67654
14.3%
o 58676
12.4%
t 55411
11.7%
g 46513
9.8%
i 46513
9.8%
a 37348
7.9%
r 33304
7.0%
s 22107
 
4.7%
l 21141
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
R 46513
45.6%
C 21141
20.7%
E 16207
 
15.9%
N 12163
 
11.9%
W 5900
 
5.8%
Space Separator
ValueCountFrequency (%)
55411
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 576308
91.2%
Common 55411
 
8.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 73554
12.8%
n 67654
11.7%
o 58676
10.2%
t 55411
9.6%
R 46513
8.1%
g 46513
8.1%
i 46513
8.1%
a 37348
6.5%
r 33304
 
5.8%
s 22107
 
3.8%
Other values (6) 88715
15.4%
Common
ValueCountFrequency (%)
55411
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 631719
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 73554
11.6%
n 67654
10.7%
o 58676
9.3%
t 55411
8.8%
55411
8.8%
R 46513
7.4%
g 46513
7.4%
i 46513
7.4%
a 37348
 
5.9%
r 33304
 
5.3%
Other values (7) 110822
17.5%

Planning Area
Categorical

Distinct39
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size363.5 KiB
Hougang
3813 
Bedok
3588 
Geylang
 
2573
Bukit Timah
 
2395
Queenstown
 
2377
Other values (34)
31767 

Length

Max length16
Median length12
Mean length8.5668738
Min length5

Characters and Unicode

Total characters398471
Distinct characters44
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBedok
2nd rowHougang
3rd rowPasir Ris
4th rowDowntown Core
5th rowBukit Merah

Common Values

ValueCountFrequency (%)
Hougang 3813
 
8.2%
Bedok 3588
 
7.7%
Geylang 2573
 
5.5%
Bukit Timah 2395
 
5.1%
Queenstown 2377
 
5.1%
Clementi 2091
 
4.5%
Tampines 2066
 
4.4%
Serangoon 2038
 
4.4%
Toa Payoh 1954
 
4.2%
Sengkang 1877
 
4.0%
Other values (29) 21741
46.7%

Length

2023-04-19T20:19:23.890616image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bukit 6129
 
9.6%
hougang 3813
 
6.0%
bedok 3588
 
5.6%
geylang 2573
 
4.0%
timah 2395
 
3.7%
queenstown 2377
 
3.7%
clementi 2091
 
3.3%
tampines 2066
 
3.2%
serangoon 2038
 
3.2%
toa 1954
 
3.1%
Other values (42) 34844
54.6%

Most occurring characters

ValueCountFrequency (%)
a 41414
 
10.4%
n 40759
 
10.2%
e 32818
 
8.2%
o 29948
 
7.5%
g 24017
 
6.0%
i 23295
 
5.8%
17369
 
4.4%
u 16597
 
4.2%
t 14347
 
3.6%
k 12953
 
3.3%
Other values (34) 144954
36.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 317234
79.6%
Uppercase Letter 63868
 
16.0%
Space Separator 17369
 
4.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 41414
13.1%
n 40759
12.8%
e 32818
10.3%
o 29948
9.4%
g 24017
 
7.6%
i 23295
 
7.3%
u 16597
 
5.2%
t 14347
 
4.5%
k 12953
 
4.1%
l 12498
 
3.9%
Other values (12) 68588
21.6%
Uppercase Letter
ValueCountFrequency (%)
B 12156
19.0%
T 7513
11.8%
P 5868
9.2%
S 5290
8.3%
C 4926
7.7%
H 3813
 
6.0%
R 3635
 
5.7%
M 3607
 
5.6%
K 3136
 
4.9%
G 2573
 
4.0%
Other values (11) 11351
17.8%
Space Separator
ValueCountFrequency (%)
17369
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 381102
95.6%
Common 17369
 
4.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 41414
 
10.9%
n 40759
 
10.7%
e 32818
 
8.6%
o 29948
 
7.9%
g 24017
 
6.3%
i 23295
 
6.1%
u 16597
 
4.4%
t 14347
 
3.8%
k 12953
 
3.4%
l 12498
 
3.3%
Other values (33) 132456
34.8%
Common
ValueCountFrequency (%)
17369
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 398471
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 41414
 
10.4%
n 40759
 
10.2%
e 32818
 
8.2%
o 29948
 
7.5%
g 24017
 
6.0%
i 23295
 
5.8%
17369
 
4.4%
u 16597
 
4.2%
t 14347
 
3.6%
k 12953
 
3.3%
Other values (34) 144954
36.4%

Interactions

2023-04-19T20:19:21.266586image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:18.484070image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:18.866046image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:19.247645image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:19.651371image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:20.066104image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:20.461843image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:20.849846image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:21.319815image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:18.528605image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:18.913468image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:19.296196image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:19.705265image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:20.116465image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:20.507478image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:20.900691image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:21.370204image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:18.572629image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:18.956293image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:19.342831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:19.762152image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:20.163112image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:20.551137image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:20.949596image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:21.425014image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:18.618644image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:19.003027image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:19.391264image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:19.815297image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:20.211646image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:20.599817image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:21.002677image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:21.477796image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:18.663679image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:19.051261image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:19.439938image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:19.862567image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:20.260874image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:20.647764image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:21.053017image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:21.531658image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:18.710160image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:19.096813image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:19.489333image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:19.911539image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:20.307918image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:20.696517image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:21.102708image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:21.583687image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:18.757754image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:19.144027image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:19.538169image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:19.959241image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:20.355732image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:20.743407image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:21.151997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:21.642167image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:18.813085image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:19.194735image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:19.591234image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:20.010728image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:20.408342image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:20.795919image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:21.208847image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-04-19T20:19:23.936626image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
No. of UnitsArea (sqm)Transacted Price ($)Unit Price ($ psm)Unit Price ($ psf)Postal DistrictPostal SectorPostal CodeType of AreaProperty TypeCompletion DateType of SalePurchaser Address IndicatorPlanning RegionPlanning Area
No. of Units1.0000.0730.0730.0400.040-0.021-0.021-0.0210.0000.0000.0540.0090.0130.0060.026
Area (sqm)0.0731.0000.732-0.312-0.3120.0680.0660.0720.0000.0000.0610.0090.0100.0050.030
Transacted Price ($)0.0730.7321.0000.3460.346-0.337-0.332-0.3280.0000.0000.0620.0120.0130.0090.040
Unit Price ($ psm)0.040-0.3120.3461.0001.000-0.589-0.578-0.5800.0450.2320.1680.1820.1680.3290.335
Unit Price ($ psf)0.040-0.3120.3461.0001.000-0.589-0.578-0.5800.0450.2330.1690.1820.1680.3290.335
Postal District-0.0210.068-0.337-0.589-0.5891.0000.9880.9870.1860.2480.2160.2620.2360.7240.848
Postal Sector-0.0210.066-0.332-0.578-0.5780.9881.0001.0000.1630.2410.2300.2570.2390.6760.866
Postal Code-0.0210.072-0.328-0.580-0.5800.9871.0001.0000.1640.2420.2300.2530.2370.6750.863
Type of Area0.0000.0000.0000.0450.0450.1860.1630.1641.0000.9020.6350.2210.1760.1060.303
Property Type0.0000.0000.0000.2320.2330.2480.2410.2420.9021.0000.3230.2530.2120.2740.394
Completion Date0.0540.0610.0620.1680.1690.2160.2300.2300.6350.3231.0000.7100.3680.2250.196
Type of Sale0.0090.0090.0120.1820.1820.2620.2570.2530.2210.2530.7101.0000.3520.1440.382
Purchaser Address Indicator0.0130.0100.0130.1680.1680.2360.2390.2370.1760.2120.3680.3521.0000.1760.291
Planning Region0.0060.0050.0090.3290.3290.7240.6760.6750.1060.2740.2250.1440.1761.0001.000
Planning Area0.0260.0300.0400.3350.3350.8480.8660.8630.3030.3940.1960.3820.2911.0001.000

Missing values

2023-04-19T20:19:21.765910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-19T20:19:21.960801image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Project NameAddressNo. of UnitsArea (sqm)Type of AreaTransacted Price ($)Nett Price($)Unit Price ($ psm)Unit Price ($ psf)Sale DateProperty TypeTenureCompletion DateType of SalePurchaser Address IndicatorPostal DistrictPostal SectorPostal CodePlanning RegionPlanning Area
0THE BAYSHORE22 Bayshore Road #03-02188Strata888000-1009193728-FEB-2019Condominium99 Yrs From 31/05/19931996ResalePrivate1646469970East RegionBedok
1KINGSFORD WATERBAY66 Upper Serangoon View #16-121102Strata1280000-12549116628-FEB-2019Apartment99 Yrs From 03/03/20142018Sub SalePrivate1953533885North East RegionHougang
2THE JOVELL13 Flora Drive #02-11142Strata615000-14643136028-FEB-2019Condominium99 Yrs From 08/03/2018UncompletedNew SaleN.A1750506853East RegionPasir Ris
3V ON SHENTON5A Shenton Way #44-121112Strata2855680284768025426236228-FEB-2019Apartment99 Yrs From 29/11/20112017New SaleN.A1668814Central RegionDowntown Core
4THE BEACON130 Cantonment Road #10-041103Strata1570000-15243141628-FEB-2019Apartment99 Yrs From 05/03/20042008ResalePrivate2889775Central RegionBukit Merah
5WATERBANK AT DAKOTA78 Dakota Crescent #09-16158Strata1080000-18621173028-FEB-2019Condominium99 Yrs From 07/12/20092013ResalePrivate1439399945Central RegionGeylang
6MARGARET VILLE20 Margaret Drive #15-03185Strata1560650-18361170628-FEB-2019Apartment99 Yrs From 13/03/2017UncompletedNew SaleN.A314149312Central RegionQueenstown
7PARK COLONIAL4 Woodleigh Lane #10-12163Strata1359000-21571200428-FEB-2019Condominium99 Yrs From 11/10/2017UncompletedNew SaleN.A1335357686Central RegionToa Payoh
8BREEZE BY THE EAST316 Upper East Coast Road #04-031116Strata1530000-13190122528-FEB-2019CondominiumFreehold2011ResalePrivate1646465520East RegionBedok
9THE TRE VER64 Potong Pasir Avenue 1 #14-24165Strata1127112-17340161128-FEB-2019Condominium99 Yrs From 27/03/2018UncompletedNew SaleN.A1335358393Central RegionToa Payoh
Project NameAddressNo. of UnitsArea (sqm)Type of AreaTransacted Price ($)Nett Price($)Unit Price ($ psm)Unit Price ($ psf)Sale DateProperty TypeTenureCompletion DateType of SalePurchaser Address IndicatorPostal DistrictPostal SectorPostal CodePlanning RegionPlanning Area
46503EAST MEADOWS32 Tanah Merah Kechil Road #03-231111Strata1090000-982091201-AUG-2018Condominium99 Yrs From 02/03/19982002ResaleHDB1646465559East RegionBedok
46504SIMSVILLE10 Geylang East Avenue 2 #03-08191Strata960000-1054998001-AUG-2018Condominium99 Yrs From 01/12/19941998ResalePrivate1438389758Central RegionGeylang
46505THE COAST AT SENTOSA COVE276 Ocean Drive #03-261244Strata4688000-19213178501-AUG-2018Condominium99 Yrs From 11/04/20062009ResalePrivate4998449Central RegionSouthern Islands
46506CLEMENTI GREEN38 Clementi Crescent1228Land3050000-13401124501-AUG-2018Terrace HouseFreehold1978ResalePrivate2159599552Central RegionBukit Timah
46507CASTLE GREEN485 Yio Chu Kang Road #04-16188Strata810000-920585501-AUG-2018Condominium99 Yrs From 01/12/19931997ResalePrivate2678787058North East RegionAng Mo Kio
46508PRIVE37 Punggol Field #03-36177Strata828000-1075399901-AUG-2018Executive Condominium99 Yrs From 14/09/20102013ResaleHDB1982828809North East RegionPunggol
46509PARK GREEN12 Rivervale Link #17-211193Strata1470000-761770801-AUG-2018Executive Condominium99 Yrs From 17/08/20012004ResaleHDB1954545045North East RegionSengkang
46510PRIVE37 Punggol Field #09-35177Strata770000-1000092901-AUG-2018Executive Condominium99 Yrs From 14/09/20102013ResalePrivate1982828809North East RegionPunggol
46511EIGHT COURTYARDS12 Canberra Drive #11-241106Strata1100000-1037796401-AUG-2018Condominium99 Yrs From 20/09/20102014ResalePrivate2776768094North RegionYishun
46512BARTLEY RESIDENCES3A Lorong How Sun #09-19199Strata1420000-14343133301-AUG-2018Apartment99 Yrs From 29/06/20112015ResalePrivate1953536561North East RegionSerangoon